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» Adaptive metric dimensionality reduction
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VLDB
2007
ACM
174views Database» more  VLDB 2007»
14 years 4 months ago
An adaptive and dynamic dimensionality reduction method for high-dimensional indexing
Abstract The notorious "dimensionality curse" is a wellknown phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approa...
Heng Tao Shen, Xiaofang Zhou, Aoying Zhou
DATAMINE
2007
79views more  DATAMINE 2007»
13 years 4 months ago
Locally adaptive metrics for clustering high dimensional data
Carlotta Domeniconi, Dimitrios Gunopulos, Sheng Ma...
SIGMOD
2001
ACM
184views Database» more  SIGMOD 2001»
14 years 4 months ago
Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases
Similarity search in large time series databases has attracted much research interest recently. It is a difficult problem because of the typically high dimensionality of the data....
Eamonn J. Keogh, Kaushik Chakrabarti, Sharad Mehro...
ICDM
2002
IEEE
158views Data Mining» more  ICDM 2002»
13 years 9 months ago
Adaptive dimension reduction for clustering high dimensional data
It is well-known that for high dimensional data clustering, standard algorithms such as EM and the K-means are often trapped in local minimum. Many initialization methods were pro...
Chris H. Q. Ding, Xiaofeng He, Hongyuan Zha, Horst...
CVPR
2006
IEEE
14 years 6 months ago
Dimensionality Reduction by Learning an Invariant Mapping
Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that "similar" points in input space are mapped to ne...
Raia Hadsell, Sumit Chopra, Yann LeCun